We’re looking forward to presenting at the ACE22 Conference in San Antonio on Monday, June 13 at 10:50 am. Our session titled “Minimizing Equipment Failure Risk by Building a Data-Informed Culture” will take place during the International Council Session.
Unknown equipment failure and unscheduled downtime create unnecessary risks for the water and wastewater industry as they provide uninterrupted, cost-effective, and safe water for surrounding communities. Utilities use various methods and operational processes, including scheduled maintenance and real-time monitoring, to minimize the potential for failure. Unfortunately, these methods are insufficient in countering non-time-based failures and reduced lead time. With limited insight into equipment health, utilities are exposed to additional operational challenges including increased maintenance costs, decreased reliability, and unnecessary safety hazards.
As utilities have access to more quality data, plants have incorporated additional data analysis and collaboration into day-to-day operations to improve real-time insights into current equipment statuses. One approach for improved insights is to combine existing sensor data collected by Process Control, Supervisory Control and Data Acquisition (SCADA), and historian systems with advanced pattern recognition and machine learning technology to create an equipment health index. With this index, utilities uncover potential and hidden failures through early warnings of degrading equipment health. Using advanced anomaly detection, organizations expand their maintenance strategy to include predictive maintenance and thereby better manage, prioritize, and eliminate equipment failures across the system.
Beyond the technology itself, the existing culture around data and decision-making plays a significant role in successfully minimizing failure risks. Though this type of technology provides continuous insights into the health of the equipment, organizations must incorporate the intelligence into their day-to-day operations and decision-making. By implementing change management best practices and creating an organizational culture that views data as an asset, utilities can reduce equipment failure risks and improve operations.
This presentation will detail:
- Why water and wastewater utilities use early warnings to avoid failures, improve equipment reliability, and prioritize maintenance activities
- How unsupervised machine learning identifies hidden and potential failures, including operational case studies of identified anomalies and resulting outcomes
- When is an organization’s data and technology infrastructure ready to use advanced pattern recognition and machine learning
- Who is involved in creating a data culture and what are the best practices related to this type of technology and change management
- What are the 5 main steps to creating a data culture around failure reduction (definition, investigation, selection, implementation, and iteration)